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Unlocking AMC Insights Series: Leveraging Media Overlap Analysis for Enhanced Conversions

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AMC Media Mix

Amazon


By Tinuiti Team

In today’s data-driven marketing landscape, the ability to ask the right questions is paramount. Amazon Marketing Cloud (AMC) emerges as the magic 8-ball of advertising solutions, offering advertisers a robust platform for precise analytics and strategic decision-making. If you’re new to AMC, it’s a secure, privacy-friendly, dedicated cloud-based measurement and analytics solution introduced in 2021.

Understanding the Value of Amazon Marketing Cloud

Built on Amazon Web Services (AWS), AMC provides a flexible environment that empowers advertisers with customizable reporting capabilities based on event-level data across various data sets. These data sets can encompass both advertiser data and Amazon Advertising data, granting advertisers a comprehensive view of campaign performance. In essence, AMC equips advertisers with transparent, cross-channel data essential for making informed marketing decisions, a necessity in today’s marketing landscape.

For a comprehensive understanding of AMC basics, Tinuiti’s AMC overview provides all the essential information about the Amazon Marketing Cloud.

This article marks the first of a 3-part series where we dive into specific AMC use cases. In this installment, we focus on the Media Overlap analysis, guiding you through utilizing this report to address critical business questions, pinpoint key metrics, and strategically apply derived insights.

What is the Media Overlap Analysis? 

The Media Overlap analysis determines the collective impact of Amazon ads and isolates the incremental impact of a specific media type. The metrics provided by this report analyze reach and performance across a full-funnel strategy, including DSP Display, Streaming TV, and Sponsored Ads. 

To utilize this report, it is required to have data from at least two of the aforementioned ad types in a single AMC instance. The same products must be advertised in each ad type, and each ad product must have been running for at least one week during the same time period. It is recommended to wait 14 days after the query’s end date to use this analysis to capture all conversions due to Amazon’s 14-day attribution window. This use case is designed to help answer business questions surrounding how to best leverage the array of Amazon Ad products.

Here are a few examples of the types of questions the Media Overlap analysis addresses:

  • When shoppers are exposed to any combination of Display, Streaming TV, Sponsored Ads, what is the impact on conversion rates?
  • What impact does each ad type have on conversion beyond ROAS or last-touch attribution?
  • What is the average order value when shoppers are exposed to a combination of ad types?

The following metrics tend to be the most useful in addressing the business questions above:

  • Purchase rate: Percentage of unique users who purchased at least one time compared to unique users reached
  • Reach: Number of unique users reached
  • Users that purchased: Number of unique users who purchased at least one time.
  • Purchases: Number of times any amount of a promoted product or products are included in a purchase event. Purchase events include video rentals and new Subscribe & Save subscriptions.
  • Order value: Total amount resulting from a single purchase event

Below is a sample case study used to address the following question: When shoppers are exposed to any combination of Display, Streaming TV, Sponsored Ads, what is the impact on conversion rates?

Here is an example of a what a finalized report looks like: 

Top 7 Media Type Mixes based on Purchase Volume (CE Advertiser) 

Unlocking AMC Insights Series Leveraging Media Overlap Analysis for Enhanced

To answer the original question, the key metric to review here is the Prospective Purchase Rate (PPR). When exposed to fewer than three ad types, the PPR is significantly lower. However, when exposed to three or more ad types, the PPR increases. For users who were exposed to Sponsored Display (SD), Sponsored Products (SP), Demand Side Platform (DSP), and Sponsored Brands (SB) ads, the PPR was 8.19%, demonstrating the correlation between the number of ad types shoppers were exposed to and an increased Prospective Purchase Rate.

As a result of these findings, two prominent potential opportunities to improve performance emerge:

  1. Continuing to invest, or increasing investments, in DSP and video as they are key drivers in a user’s path to conversion. The advertiser should diversify their media mix with these ad products.
  2. Due to the correlation between Sponsored Products ads in combination with other ad products and higher conversion rates, there is an additional opportunity to build an AMC audience retargeting SP clickers. This will ensure advertisers are capitalizing on shoppers moving through the lower to upper funnel in their shopping journey.

AMC’s Media Overlap Analysis: Key Takeaways and Next Steps for Enhanced Conversions

AMC’s Media Overlap analysis highlights the impact of middle and upper funnel ads on conversion rates. Tinuiti’s teams observe many brands prioritizing Sponsored Products due to their perceived low risk and high returns under Amazon’s last-touch attribution model. However, this approach overlooks the influence of other ad types. Data from this analysis underscores the effectiveness of a holistic strategy. While a Sponsored Products ad may lead to a sale, it doesn’t consider other ad exposures that shape purchase decisions. The Overlap analysis underscores the value of a full-funnel strategy and the impact of DSP media on overall performance. Advertisers should consider adjusting budget allocations to DSP and streaming video based on these insights.

Furthermore, a full-funnel strategy can drive higher average order value.

The average order value significantly increases when exposed to a media mix of three or more ad types. While each advertiser should analyze their own business, Tinuiti consistently observes that users exposed to a greater number of ad products typically correlate with higher conversion rates and higher order values.

The Media Overlap analysis is part of the Instructional Query Library (IQL), which offers pre-built templates by Amazon to get started with the basics. If you’re seeking deeper insights with the guidance of experts who understand AMC’s unique landscape, reach out to Tinuiti today.

Liked this article? Don’t miss Part 2 of our AMC use case series on Tinuiti’s blog next month!

This post was authored by Averie Lynch, Specialist of Strategic Services at Tinuiti.

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YouTube Ad Specs, Sizes, and Examples [2024 Update]

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YouTube Ad Specs, Sizes, and Examples

Introduction

With billions of users each month, YouTube is the world’s second largest search engine and top website for video content. This makes it a great place for advertising. To succeed, advertisers need to follow the correct YouTube ad specifications. These rules help your ad reach more viewers, increasing the chance of gaining new customers and boosting brand awareness.

Types of YouTube Ads

Video Ads

  • Description: These play before, during, or after a YouTube video on computers or mobile devices.
  • Types:
    • In-stream ads: Can be skippable or non-skippable.
    • Bumper ads: Non-skippable, short ads that play before, during, or after a video.

Display Ads

  • Description: These appear in different spots on YouTube and usually use text or static images.
  • Note: YouTube does not support display image ads directly on its app, but these can be targeted to YouTube.com through Google Display Network (GDN).

Companion Banners

  • Description: Appears to the right of the YouTube player on desktop.
  • Requirement: Must be purchased alongside In-stream ads, Bumper ads, or In-feed ads.

In-feed Ads

  • Description: Resemble videos with images, headlines, and text. They link to a public or unlisted YouTube video.

Outstream Ads

  • Description: Mobile-only video ads that play outside of YouTube, on websites and apps within the Google video partner network.

Masthead Ads

  • Description: Premium, high-visibility banner ads displayed at the top of the YouTube homepage for both desktop and mobile users.

YouTube Ad Specs by Type

Skippable In-stream Video Ads

  • Placement: Before, during, or after a YouTube video.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
    • Action: 15-20 seconds

Non-skippable In-stream Video Ads

  • Description: Must be watched completely before the main video.
  • Length: 15 seconds (or 20 seconds in certain markets).
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Vertical: 9:16
    • Square: 1:1

Bumper Ads

  • Length: Maximum 6 seconds.
  • File Format: MP4, Quicktime, AVI, ASF, Windows Media, or MPEG.
  • Resolution:
    • Horizontal: 640 x 360px
    • Vertical: 480 x 360px

In-feed Ads

  • Description: Show alongside YouTube content, like search results or the Home feed.
  • Resolution:
    • Horizontal: 1920 x 1080px
    • Vertical: 1080 x 1920px
    • Square: 1080 x 1080px
  • Aspect Ratio:
    • Horizontal: 16:9
    • Square: 1:1
  • Length:
    • Awareness: 15-20 seconds
    • Consideration: 2-3 minutes
  • Headline/Description:
    • Headline: Up to 2 lines, 40 characters per line
    • Description: Up to 2 lines, 35 characters per line

Display Ads

  • Description: Static images or animated media that appear on YouTube next to video suggestions, in search results, or on the homepage.
  • Image Size: 300×60 pixels.
  • File Type: GIF, JPG, PNG.
  • File Size: Max 150KB.
  • Max Animation Length: 30 seconds.

Outstream Ads

  • Description: Mobile-only video ads that appear on websites and apps within the Google video partner network, not on YouTube itself.
  • Logo Specs:
    • Square: 1:1 (200 x 200px).
    • File Type: JPG, GIF, PNG.
    • Max Size: 200KB.

Masthead Ads

  • Description: High-visibility ads at the top of the YouTube homepage.
  • Resolution: 1920 x 1080 or higher.
  • File Type: JPG or PNG (without transparency).

Conclusion

YouTube offers a variety of ad formats to reach audiences effectively in 2024. Whether you want to build brand awareness, drive conversions, or target specific demographics, YouTube provides a dynamic platform for your advertising needs. Always follow Google’s advertising policies and the technical ad specs to ensure your ads perform their best. Ready to start using YouTube ads? Contact us today to get started!

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Why We Are Always ‘Clicking to Buy’, According to Psychologists

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Why We Are Always 'Clicking to Buy', According to Psychologists

Amazon pillows.

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A deeper dive into data, personalization and Copilots

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A deeper dive into data, personalization and Copilots

Salesforce launched a collection of new, generative AI-related products at Connections in Chicago this week. They included new Einstein Copilots for marketers and merchants and Einstein Personalization.

To better understand, not only the potential impact of the new products, but the evolving Salesforce architecture, we sat down with Bobby Jania, CMO, Marketing Cloud.

Dig deeper: Salesforce piles on the Einstein Copilots

Salesforce’s evolving architecture

It’s hard to deny that Salesforce likes coming up with new names for platforms and products (what happened to Customer 360?) and this can sometimes make the observer wonder if something is brand new, or old but with a brand new name. In particular, what exactly is Einstein 1 and how is it related to Salesforce Data Cloud?

“Data Cloud is built on the Einstein 1 platform,” Jania explained. “The Einstein 1 platform is our entire Salesforce platform and that includes products like Sales Cloud, Service Cloud — that it includes the original idea of Salesforce not just being in the cloud, but being multi-tenancy.”

Data Cloud — not an acquisition, of course — was built natively on that platform. It was the first product built on Hyperforce, Salesforce’s new cloud infrastructure architecture. “Since Data Cloud was on what we now call the Einstein 1 platform from Day One, it has always natively connected to, and been able to read anything in Sales Cloud, Service Cloud [and so on]. On top of that, we can now bring in, not only structured but unstructured data.”

That’s a significant progression from the position, several years ago, when Salesforce had stitched together a platform around various acquisitions (ExactTarget, for example) that didn’t necessarily talk to each other.

“At times, what we would do is have a kind of behind-the-scenes flow where data from one product could be moved into another product,” said Jania, “but in many of those cases the data would then be in both, whereas now the data is in Data Cloud. Tableau will run natively off Data Cloud; Commerce Cloud, Service Cloud, Marketing Cloud — they’re all going to the same operational customer profile.” They’re not copying the data from Data Cloud, Jania confirmed.

Another thing to know is tit’s possible for Salesforce customers to import their own datasets into Data Cloud. “We wanted to create a federated data model,” said Jania. “If you’re using Snowflake, for example, we more or less virtually sit on your data lake. The value we add is that we will look at all your data and help you form these operational customer profiles.”

Let’s learn more about Einstein Copilot

“Copilot means that I have an assistant with me in the tool where I need to be working that contextually knows what I am trying to do and helps me at every step of the process,” Jania said.

For marketers, this might begin with a campaign brief developed with Copilot’s assistance, the identification of an audience based on the brief, and then the development of email or other content. “What’s really cool is the idea of Einstein Studio where our customers will create actions [for Copilot] that we hadn’t even thought about.”

Here’s a key insight (back to nomenclature). We reported on Copilot for markets, Copilot for merchants, Copilot for shoppers. It turns out, however, that there is just one Copilot, Einstein Copilot, and these are use cases. “There’s just one Copilot, we just add these for a little clarity; we’re going to talk about marketing use cases, about shoppers’ use cases. These are actions for the marketing use cases we built out of the box; you can build your own.”

It’s surely going to take a little time for marketers to learn to work easily with Copilot. “There’s always time for adoption,” Jania agreed. “What is directly connected with this is, this is my ninth Connections and this one has the most hands-on training that I’ve seen since 2014 — and a lot of that is getting people using Data Cloud, using these tools rather than just being given a demo.”

What’s new about Einstein Personalization

Salesforce Einstein has been around since 2016 and many of the use cases seem to have involved personalization in various forms. What’s new?

“Einstein Personalization is a real-time decision engine and it’s going to choose next-best-action, next-best-offer. What is new is that it’s a service now that runs natively on top of Data Cloud.” A lot of real-time decision engines need their own set of data that might actually be a subset of data. “Einstein Personalization is going to look holistically at a customer and recommend a next-best-action that could be natively surfaced in Service Cloud, Sales Cloud or Marketing Cloud.”

Finally, trust

One feature of the presentations at Connections was the reassurance that, although public LLMs like ChatGPT could be selected for application to customer data, none of that data would be retained by the LLMs. Is this just a matter of written agreements? No, not just that, said Jania.

“In the Einstein Trust Layer, all of the data, when it connects to an LLM, runs through our gateway. If there was a prompt that had personally identifiable information — a credit card number, an email address — at a mimum, all that is stripped out. The LLMs do not store the output; we store the output for auditing back in Salesforce. Any output that comes back through our gateway is logged in our system; it runs through a toxicity model; and only at the end do we put PII data back into the answer. There are real pieces beyond a handshake that this data is safe.”

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